Opioid treatment checkins
Andrew Ba Tran
October 4, 2016
Questions to ask:
- Which towns had the most admissions per capita?
- Urban versus rural versus mixed admissions? Is there a correlation between admissions and overdose deaths? (By year)
Where do people tend to die (urban, rural, mixed)
distance to clinics, pharmacies, hospitals
correlation between deaths and admissions to these distances?
treat$Admissions[is.na(treat$Admissions)] <- 0
gg <- ggplot(treat, aes(y=Admissions, x=FiscalYear))
gg <- gg + geom_bar(stat="identity")
gg <- gg + facet_wrap(~Town, ncol=4, scale="free_x")
#gg <- gg + facet_wrap(~state, scale="free", ncol=5)
#gg <- gg + scale_x_continuous(limits=c(2012,2016), breaks=c(2012,2016),
# labels=c("2012", "2016"))
gg <- gg + labs(x=NULL, y=NULL,
title="Opioid Related Treatment Admissions by Town",
subtitle="",
caption="Department of Mental Health and Addiction Services")
gg <- gg + theme_minimal(base_family="Lato Regular")
gg <- gg + theme(panel.grid.major.y=element_blank())
gg <- gg + theme(panel.grid.minor=element_blank())
gg <- gg + theme(plot.title=element_text(family="Lato Black"))
gg
# gg <- ggplot(treat, aes(y=Unduplicated.Clients, x=FiscalYear))
# gg <- gg + geom_bar(stat="identity")
# gg <- gg + facet_wrap(~Town, ncol=4, scale="free_x")
# #gg <- gg + facet_wrap(~state, scale="free", ncol=5)
# #gg <- gg + scale_x_continuous(limits=c(2012,2016), breaks=c(2012,2016),
# # labels=c("2012", "2016"))
# gg <- gg + labs(x=NULL, y=NULL,
# title="Opioid Related Treatment Admissions (unduplicated clients) by Town",
# subtitle="",
# caption="Department of Mental Health and Addiction Services")
# gg <- gg + theme_minimal(base_family="Lato Regular")
# gg <- gg + theme(panel.grid.major.y=element_blank())
# gg <- gg + theme(panel.grid.minor=element_blank())
# gg <- gg + theme(plot.title=element_text(family="Lato Black"))
# ggtreat$Admissions[is.na(treat$Admissions)] <- 0
treat <- ctpopulator(Town, treat)## [1] "Checking to see if names match..."treat$per_capita_a <- round(treat$Admissions/treat$pop2013*1000, 2)
gg <- ggplot(treat, aes(y=per_capita_a, x=FiscalYear))
gg <- gg + geom_bar(stat="identity")
gg <- gg + facet_wrap(~Town, ncol=4, scale="free_x")
#gg <- gg + facet_wrap(~state, scale="free", ncol=5)
#gg <- gg + scale_x_continuous(limits=c(2012,2016), breaks=c(2012,2016),
# labels=c("2012", "2016"))
gg <- gg + labs(x=NULL, y=NULL,
title="Opioid Related Treatment Admissions per 1,000 residents by Town",
subtitle="",
caption="Department of Mental Health and Addiction Services")
gg <- gg + theme_minimal(base_family="Lato Regular")
gg <- gg + theme(panel.grid.major.y=element_blank())
gg <- gg + theme(panel.grid.minor=element_blank())
gg <- gg + theme(plot.title=element_text(family="Lato Black"))
gg
Mapped
town_shape <- readOGR(dsn="maps", layer="ctgeo")## OGR data source with driver: ESRI Shapefile
## Source: "maps", layer: "ctgeo"
## with 169 features
## It has 6 fieldstown_shape_df <- fortify(town_shape, region="NAME10")
names(treat)[names(treat) == 'Town'] <- 'id'
treat$id <- str_to_title(treat$id)
town_map <- left_join(town_shape_df, treat)
gg <- ggplot(town_map, aes(long,lat, group=group, fill=per_capita_a))
gg <- gg + geom_polygon()
gg <- gg + geom_path(color = "grey73")
gg <- gg + coord_equal()
gg <- gg + facet_wrap(~FiscalYear, ncol=2)
gg <- gg + scale_fill_gradient(low="grey73", high="blue")
gg <- gg + labs(x=NULL, y=NULL, title="Opioid Related Treatment Admissions by town",
subtitle="Per 1,000 residents",
caption="SOURCE: Department of Mental Health and Addiction Services\nAndrew Ba Tran/TrendCT.org")
gg <- gg + theme_bw(base_family="Lato Regular")
gg <- gg + theme(text = element_text(size=16))
gg <- gg + theme(panel.grid.major=element_blank())
gg <- gg + theme(panel.grid.minor=element_blank())
gg <- gg + theme(panel.border=element_blank())
gg <- gg + theme(axis.ticks=element_blank())
gg <- gg + theme(axis.text.x=element_blank())
gg <- gg + theme(axis.text.y=element_blank())
gg <- gg + theme(plot.title=element_text(face="bold", family="Lato Black", size=22))
gg <- gg + theme(plot.subtitle=element_text(face="italic", size=9, margin=margin(b=12)))
gg <- gg + theme(plot.caption=element_text(size=12, margin=margin(t=10, r=80), color="#7a7d7e"))
gg <- gg + theme(plot.margin = unit(c(1, 1, 1, 1), "lines"))
gg
Towns with most check-ins per capita
tab1 <- treat %>%
select(id, FiscalYear, per_capita_a) %>%
spread(FiscalYear, per_capita_a) %>%
arrange(-`2016`)
kable(head(tab1, 10))| id | 2012 | 2013 | 2014 | 2015 | 2016 |
|---|---|---|---|---|---|
| Canaan | 28.75 | 24.39 | 20.91 | 32.23 | 31.36 |
| Windham | 16.87 | 20.26 | 22.36 | 27.53 | 25.08 |
| Torrington | 10.40 | 13.37 | 16.06 | 19.94 | 23.74 |
| New London | 16.09 | 17.40 | 23.27 | 18.92 | 20.41 |
| Hartford | 18.17 | 17.59 | 18.60 | 20.34 | 19.12 |
| Sharon | 10.47 | 13.00 | 14.08 | 15.53 | 18.06 |
| Griswold | 10.29 | 12.54 | 15.30 | 15.72 | 16.97 |
| Waterbury | 12.08 | 12.47 | 12.90 | 16.16 | 16.66 |
| Hampton | 5.51 | 14.88 | 6.06 | 13.77 | 16.53 |
| Sprague | 11.72 | 11.72 | 16.08 | 15.07 | 16.08 |
Check-ins by town type
source("urban_rural_mixed.R")
town_count <- town_count[c("NAME10", "Type", "perc_urban")]
colnames(town_count) <- c("id", "town_type", "perc_urban")
tab2 <- treat %>%
select(id, FiscalYear, Admissions, pop2013, per_capita_a)
tab2_joined <- left_join(tab2, town_count)
gg <- ggplot(tab2_joined, aes(x=town_type, y=Admissions,fill=town_type))
gg <- gg + geom_boxplot()
gg <- gg + facet_wrap(~FiscalYear, ncol=5)
gg <- gg + labs(x=NULL, y="Admissions", title="Overall opioid-related checkins in Connecticut",
subtitle="Per 1,000 residents",
caption="SOURCE: Department of Mental Health and Addiction Services\nAndrew Ba Tran/TrendCT.org")
gg <- gg + theme_bw(base_family="Lato Regular")
gg <- gg + theme(text = element_text(size=16))
gg <- gg + theme(panel.grid.major=element_blank())
gg <- gg + theme(panel.grid.minor=element_blank())
gg <- gg + theme(panel.border=element_blank())
gg <- gg + theme(axis.text.x=element_text(angle=90, vjust=0.4,hjust=1))
#gg <- gg + theme(axis.ticks=element_blank())
#gg <- gg + theme(axis.text.y=element_blank())
gg <- gg + theme(plot.title=element_text(face="bold", family="Lato Black", size=22))
gg <- gg + theme(plot.subtitle=element_text(face="italic", size=9, margin=margin(b=12)))
gg <- gg + theme(plot.caption=element_text(size=12, margin=margin(t=10, r=80), color="#7a7d7e"))
gg <- gg + theme(plot.margin = unit(c(1, 1, 1, 1), "lines"))
gg
p <- ggplot(tab2_joined, aes(x=town_type, y=per_capita_a,fill=town_type))
gg <- gg + geom_boxplot()
gg <- gg + facet_grid(.~FiscalYear)
gg <- gg + labs(x=NULL, y="Admissions per capita", title="Overall opioid-related checkins in Connecticut",
subtitle="Per 1,000 residents",
caption="SOURCE: Department of Mental Health and Addiction Services\nAndrew Ba Tran/TrendCT.org")
gg <- gg + theme_bw(base_family="Lato Regular")
gg <- gg + theme(text = element_text(size=16))
gg <- gg + theme(panel.grid.major=element_blank())
gg <- gg + theme(panel.grid.minor=element_blank())
gg <- gg + theme(panel.border=element_blank())
gg <- gg + theme(axis.text.x=element_text(angle=90, vjust=0.4,hjust=1))
#gg <- gg + theme(axis.ticks=element_blank())
#gg <- gg + theme(axis.text.y=element_blank())
gg <- gg + theme(plot.title=element_text(face="bold", family="Lato Black", size=22))
gg <- gg + theme(plot.subtitle=element_text(face="italic", size=9, margin=margin(b=12)))
gg <- gg + theme(plot.caption=element_text(size=12, margin=margin(t=10, r=80), color="#7a7d7e"))
gg <- gg + theme(plot.margin = unit(c(1, 1, 1, 1), "lines"))
gg